See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: unsloth/llama-3-8b-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 074e5e5cf600c32a_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/074e5e5cf600c32a_train_data.json
  type:
    field_input: text
    field_instruction: category
    field_output: title
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
early_stopping_threshold: 0.0001
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: romainnn/1984c72c-6d4b-47c3-988f-6f37f80befe9
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 855
micro_batch_size: 4
mlflow_experiment_name: /tmp/074e5e5cf600c32a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.014235767080073342
wandb_entity: null
wandb_mode: online
wandb_name: fe8ccfcb-a600-4801-baf3-7ac6ca36101b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: fe8ccfcb-a600-4801-baf3-7ac6ca36101b
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

1984c72c-6d4b-47c3-988f-6f37f80befe9

This model is a fine-tuned version of unsloth/llama-3-8b-Instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0051

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• training_steps: 855

Training results

Training Loss	Epoch	Step	Validation Loss
2.9555	0.0001	1	2.7854
1.0246	0.0092	100	1.1214
1.0484	0.0185	200	1.0990
0.9124	0.0277	300	1.0646
1.1485	0.0370	400	1.0492
0.861	0.0462	500	1.0312
1.0239	0.0555	600	1.0192
0.9774	0.0647	700	1.0084
0.932	0.0739	800	1.0051

Framework versions

• PEFT 0.13.2
• Transformers 4.46.0
• Pytorch 2.5.0+cu124
• Datasets 3.0.1
• Tokenizers 0.20.1